Imaging systems which automate assessment of images are known. However, particularly in the field of radiography, images or features of an image are often difficult to interpret.
Image processing systems process images to derive certain information, for example, from X-ray or magnetic resonance images (MRI) and diagnostic information and in particular these are used to help in the diagnosis of cancers.
The propensity towards and the onset of cancer has been associated with a variety of factors, such as, exposure to certain chemicals (e.g. asbestos, polycyclic hydrocarbons and compounds contained in cigarette smoke); exposure to ionizing radiation (e.g. x-rays, radioactive fall out and ultra violet light); certain viruses; and the influence of oncogenes.
Other factors of increasing interest include: the parasitic relationship between cancer cells and support tissue around tumours, for example fat, whereby cancerous cells release hydrogen peroxide generating free radicals in neighbouring fibroblasts which prompts the fibroblasts to digest their mitochondria, releasing nutrients that feed the cancers cells.
The relationship between tissue type and breast cancer is particularly prevalent: that is, the higher the density of fibro glandular tissue, the greater the risk of breast cancer. The interaction of the fat and fibro glandular tissue is similarly a critical factor in breast cancer risk and breast cancer development.
Diagnosis comprises both invasive and non-invasive techniques, the latter generally entailing the generation of images from image processing systems. These systems, in breast imaging and other medical diagnostic for a yield different viewing angles, compression, imaging conditions and variation in the composition of tissue over time. Commonly, such images are visually assessed, for example, by radiologists. Visual assessment on the part of a radiologist may be iterative/based on experience and consideration of a number of factors.
Often image processing systems receive images which are closely related, for example, images of the same subject with slight variation in angle of view; or images which have been captured at different times.
Such multiplicity and variety of images can infer more information than a single image. Further, selective information from multiple images can enhance reliability, for example, exposing a hitherto obstructed object. Thus availability of comparative images can help direct and verify the processing.
However, although a variety of diagnostic information is available from a number of images, the diagnostic information is generally used separately, due to the complications of automation and integration, such as the related need to compensate for varying parameters.
To elaborate in respect of mammography, comparative images might comprise cranio-caudal (CC) images and medio-lateral oblique (MLO) images; CC and MLO images of the same breast as well as other views; images of both the left and right breast; and images taken at an earlier time. Due to the size of the images (typically 25-70 Mb) image processing systems, the images are generally processed one image at a time, and the results then collated. For example, computer-aided detection (CAD) systems might indicate that a certain object is of higher confidence if it has been detected in both the CC and MLO views, or, a certain object is of higher confidence of being cancer if it was not present in the previous image, or has no matching object in the other breast.
However, accurate measurement of breast density can be significantly skewed by small fluctuations in measurement; for example, the recent use of slanted compression paddles in mammography examination entails a variation of breast thickness from the chest wall to the breast margin, in some cases up to 2 cm. Such fluctuations in breast height leads to large variations in the estimated breast density, thus slanted compression paddles incur the risk of invalidating many of the models currently used in quantification of volumetric breast density.
It is an object of the invention to avoid this variation and provide a more consistent and reliable technique of imaging so providing a more reliable diagnosis.